PhD Studentship: DRIVE Research Cluster: Intelligent and Inclusive Public Transport

Overview

Public transport in rural England suffers from a range of issues, including poor utilisation, low frequency and lack of accessibility. Because of these issues, it is neither efficient nor sustainable nor inclusive.

This project aims to reinvent public transport by using AI to predict demand and to adapt to specific requests, including the door-to-door route and any accessibility requirements. 

Based on this data, appropriate vehicles can be scheduled, either on a fixed schedule, or on a flexible schedule that takes individual requests into account. The project will consider a town and rural setting such as Loughborough and the surrounding villages and simulate the effect of intelligent dynamic scheduling on the passenger, looking especially for the impact on the different groups. 

This project is part of the prestigious Loughborough University Vice Chancellor’s PhD Cluster – Diverse Research in Inclusive Vehicle Environments Research Cluster (DRIVE-RC). The PhD candidate will join a cohort of 5 students who will be working on different multidisciplinary aspects of inclusivity within vehicular environments and will be supported by Loughborough’s Transportation AI Innovation Centre. 

We highly recommend potential students to read more about the project topic, and the TRAICE centre, think about how they can contribute to this research, and prepare a short but targeted research proposal. We strongly encourage applicants from a diverse range of backgrounds, cultures, genders, from people with a disability, or with experience as a career.

Feel free to reach out to the primary supervisor if you have any questions.

Supervisors

Primary supervisor: Thomas Steffen

Secondary supervisor: Mahroo Eftekhari 

Entry requirements

Applicants should have, or expect to achieve, at least a 2:1 or equivalent in a relevant subject.

We are specifically looking for candidates with a strong background in transport, logistics, routing, scheduling, optimisation, human factors, techno-economic analysis, and AI. 

The ideal candidate will have a working knowledge of key software tools such as MATLAB, Python, OR-Tools for high-level simulation; working with real-world data; and conducting optimisations and data analysis based on diverse datasets and data sources.

Fees and funding

The studentship is for 3 years and provides a tax-free stipend of £19,237 per annum for the duration of the studentship plus university tuition fees. 

How to apply

All applications should be made online via the above ‘Apply’ button. Under ‘programme name’, select School of Aeronautical, Automotive, Chemical and Materials Engineering. Please quote the advertised reference number: *AAE-TS-2502* in your application.

Only applicants with a complete application including CV, research proposal, personal statement, and example(s) of your written work etc will be considered for an interview. Please make sure you have uploaded all these documents.

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